app.py

from fastapi import FastAPI, UploadFile, File, Form
from fastapi.middleware.cors import CORSMiddleware
import uvicorn
import whisper
import tempfile
import os

app = FastAPI()

# Allow your Blogger site to communicate with this server
app.add_middleware(
    CORSMiddleware,
    allow_origins=["https://lyricvideogen.blogspot.com"], 
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

print("Loading AI Model (this takes a moment on startup)...")
# Using the "tiny" model so it runs fast on the free CPU tier
model = whisper.load_model("tiny") 

@app.post("/sync")
async def sync_lyrics(audio: UploadFile = File(...), lyrics: str = Form(...)):
    print(f"Processing audio: {audio.filename}")
    
    # 1. Save the uploaded audio to a temporary file so Whisper can read it
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_audio:
        temp_audio.write(await audio.read())
        temp_audio_path = temp_audio.name

    try:
        # 2. Run the AI to detect vocal timestamps
        result = model.transcribe(temp_audio_path)
        
        # 3. Process the user's custom lyrics
        user_lines = [line.strip() for line in lyrics.split('\n') if line.strip()]
        timestamps = []
        
        # 4. Map the AI's detected timestamps to the user's custom text
        for i, line in enumerate(user_lines):
            if i < len(result["segments"]):
                # Grab the start time of the vocal segment
                start_time = result["segments"][i]["start"]
            else:
                # Fallback if the user pasted more text lines than the AI heard vocals
                start_time = timestamps[-1]["time"] + 2.0 if timestamps else 0.0
            
            timestamps.append({"text": line, "time": start_time})

        return {"status": "success", "timestamps": timestamps}

    finally:
        # Clean up the server storage
        os.remove(temp_audio_path)

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)